Self-harm, somatic disorders and mortality in the 3 years following a hospitalisation in psychiatry in adolescents and young adults
Bibliographic record
Abstract
BACKGROUND: There is limited recent information regarding the risk of self-harm, somatic disorders and premature mortality following discharge from psychiatric hospital in young people. OBJECTIVE: To measure these risks in young people discharged from a psychiatric hospital as compared with both non-affected controls and non-hospitalised affected controls. METHODS: Data were extracted from the French national health records. Cases were compared with two control groups. CASES: all individuals aged 12-24 years, hospitalised in psychiatry in France in 2013-2014. Non-affected controls: matched for age and sex with cases, not hospitalised in psychiatry and no identification of a mental disorder in 2008-2014. Affected controls: unmatched youths identified with a mental disorder between 2008 and 2014, never hospitalised in psychiatry. Follow-up of 3 years. Logistic regression analyses were conducted with these confounding variables: age, sex, past hospitalisation for self-harm, past somatic disorder diagnosis. FINDINGS: The studied population comprised 73 300 hospitalised patients (53.6% males), 219 900 non-affected controls and 9 683 affected controls. All rates and adjusted risks were increased in hospitalised patients versus both non-affected and affected controls regarding a subsequent hospitalisation for self-harm (HR=105.5, 95% CIs (89.5 to 124.4) and HR=1.5, 95% CI (1.4 to 1.6)), a somatic disorder diagnosis (HR=4.1, 95% CI (3.9-4.1) and HR=1.4, 95% CI (1.3-1.5)), all-cause mortality (HR=13.3, 95% CI (10.6-16.7) and HR=2.2, 95% CI (1.5-3.0)) and suicide (HR=9.2, 95% CI (4.3-19.8) and HR=1.7, 95% CI (1.0-2.9)). CONCLUSIONS: The first 3 years following psychiatric hospital admission of young people is a period of high risk for self-harm, somatic disorders and premature mortality. CLINICAL IMPLICATIONS: Attention to these negative outcomes urgently needs to be incorporated in aftercare policies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".